Optimization of settlement land use through carbon footprint approach in The North Balikpapan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Limited land in the downtown area as well as the increasing amount of new activities centre causes residential development leads to North Balikpapan. This area is an urban fringe with vast protected forests as buffer zone and catchment area for the city and surrounding area. Land conversion in this area will increase hazard risk of inundation, water quality decrease and increased CO 2 emissions. Therefore, development should be maintained environmental stability. One of the rights applicated approach is carbon footprint that is capable to measure the balance between production and absorption needs of CO 2 emissions. To find the optimal land allocation, we used carbon footprint calculation from the household activities, identify the factors of settlement growth, and use Linear Programming analysis. Analysis’ results show that settlement activities in North Balikpapan produce 108.362,4 tCO 2 /year or equivalent with 618,50 Ha green space. Meanwhile, the development of settlement in North Balikpapan is affected by social demographic, developer initiative, environmental condition, public facilities availability, economical structure, and policy factors. According to those factors, optimal allocation of settlement area in North Balikpapan is only about 4,510.01 Ha. With that condition, it still able to absorb CO 2 emissions from inside or outside the area around 2.751 tCO 2 /year.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it